Knowledge that Transforms

[Audit planning is a crucial task in the audit process. While making planning decisions, auditors have a large amount of information available to them and their existing knowledge structures influence how this information is used to form a problem representation. This problem representation has a significant effect on all subsequent decision making. The purpose of this study is to investigate the nature and evolution of auditors' planning problem representations by an examination of free recalls. In the cognitive psychology literature, it has been reported that, as decision makers accumulate more experience, their knowledge structures change to embody more total knowledge, more knowledge of relationships, and more abstract knowledge. In the context of an audit planning task, I predicted that problem representations formed by more experienced auditors would reflect these characteristics. To test the hypotheses, an experimental study was conducted with 211 practicing auditors. Subjects were from 16 offices of the then Big Eight public accounting firms, with experience levels ranging from one month to 30 years. Using experimental materials that consisted of approximately 20 pages of background information on a hypothetical audit client, subjects completed a planning task, followed by a background questionnaire, and a surprise recall task. The free recalls provide indirect evidence on the nature of the problem representations formed during the planning task. The results suggest that the problem representations of managers and partners may be different from those of seniors or juniors. The manager/partner recalls included a smaller percentage of simple case facts and a higher number and percentage of abstractions from the basic facts. These results have potential implications for audit practice. To the extent that the managers and partners form "better" problem representations and are actively involved in the process, more effective and efficient audit planning might be possible. However, more research needs to be conducted to determine the specific effect of the exhibited differences on planning decisions.]

This paper suggests a new procedure for evaluating the fit of a dynamic structural economic model. The procedure begins by augmenting the variables in the model with just enough stochastic error so that the model can exactly match the second moments of the actual data. Measures of fit for the model can then be constructed on the basis of the size of this error. The procedure is applied to a standard real business cycle model. Over the business cycle frequencies, the model must be augmented with a substantial error to match data for the postwar U.S. economy. Lower bounds on the variance of the error range from 40 percent to 60 percent of the variance in the actual data.

This paper presents evidence on the nature of productivity growth for five major industries in six countries. Output growth is found to be more correlated across countries than productivity growth. In addition, productivity growth is more correlated across industries within one country than across countries within one industry. Using an error-components model, I find that a substantial fraction of changes in annual productivity can be attributed to nation-specific factors that are common across industries. The evidence suggests that short-run productivity growth is similar across industries in a nation but less similar across countries in any specific industry.

At the microeconomic level, durable purchases are often discontinuous and relatively large. This feature has the potential to explain why aggregate expenditure on durables responds only slowly (relative to the frictionless permanent income model) to wealth and other aggregate innovations. In this paper I develop new results on the problem of dynamic aggregation of stochastically heterogeneous units, which help to characterize the connection between microeconomic behavior and aggregate dynamics in the presence of nonconvex adjustment costs. Using these results and splitting postwar U.S. aggregate durable purchases into different subcategories and time periods, I provide further support for the view that lumpy microeconomic purchases play an important role in explaining the time-series behavior of aggregate expenditure on durable goods.

This paper attempts to integrate traditional models of invention and learning by doing, developing a model that emphasizes the interdependence between research activity in the laboratory and production experience on the factory floor. Learning depends on invention in that learning by doing is viewed as the exploration of the finite and bounded productive potential of invented technologies. At the same time, the profitability of costly invention is dependent on learning in that costs of production depend on cumulative learning experience. The model is a true hybrid, allowing for circumstances in which either the incentives to engage in research and/or the incentives to produce different goods are the binding constraints on growth.

This paper presents a class of models in which agents may devote part of their nonleisure activities to going to school so as to increase the efficiency units of labor they supply to the firms and the wages they receive. The interaction among the technology of human capital accumulation and agents' preferences will determine endogenously the economy's rate of growth. Given a constant returns to scale technology for physical capital accumulation, we characterize the set of steady states as a ray from the origin and show the global convergence of every off-balanced path to some point on this ray. Further properties concerning the dynamic evolution of the state and control variables around the ray of steady states are also established. Our analysis is useful to understand the role played by the technologies of physical and human capital in the process of accumulation and to evaluate the impact of policies geared toward attaining higher levels of capital. Our results highlight the importance of human capital in the dynamics of growth.

The paper examines learning by doing in the context of a production function in which the other arguments are labor, human capital, physical capital, and vintage as a proxy for embodied technical change in physical capital. Learning is further decomposed into organization learning, capital learning, and manual task learning. The model is tested with time-series and cross-section data for various samples of up to 2,150 plants over a 14-year period.